Automated Question-Answering aims at delivering concise information that contains answers to user questions. This paper reviews and compares three main question-answering approaches based on Natural Language Processing, Information Retrieval, and question templates, eliciting their differences and the context of application that best suits each of them.

The question-answering (QA) paradigm, i.e. the process of retrieving precise answers to natural language (NL) questions, was introduced in late 1960-ies and early 1970-ies within the framework of Artificial Intelligence. The advent of WWW and the need to provide advanced, user-friendly search tools has extended the QA paradigm to a larger audience of people and a larger number of fields, including medicine. This paper reviews and compares three main question-answering approaches based on Natural Language Processing, Information Retrieval, and question templates, eliciting their differences and the context of application that best suits each of them within the medical domain.

Abstract:This paper introduces a pilot study aimed at investigating the extraction of word relations from a sample of a medical parallel corpus in the field of Psychology. Word relations are extracted in order to create a bilingual lexicon for cross lingual question answering between Swedish and English. Four different variants of the sample corpus were utilized: word inflections with and without POS tagging, lemmas with and without POS tagging. The purpose of the study was to analyze the quality of the word relations obtained from the different versions of the corpus and to understand which version of the corpus was more suitable for extracting a bilingual lexicon in the field of psychology. The word alignments were evaluated with the help of reference data (gold standards), which were constructed before the word alignment process.

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This article describes the development and evaluation of a set of knowledge patterns that provide guidelines and implications of design for developers of mental health portals. The knowledge patterns were based on three foundations: (1) knowledge integration of language technology approaches; (2) experiments with language technology applications and (3) user studies of portal interaction. A mixed-methods approach was employed for the evaluation of the knowledge patterns: formative workshops with knowledge pattern experts and summative surveys with experts in specific domains. The formative evaluation improved the cohesion of the patterns. The results of the summative evaluation showed that the problems discussed in the patterns were relevant for the domain, and that the knowledge embedded was useful to solve them. Ten patterns out of thirteen achieved an average score above 4.0, which is a positive result that leads us to conclude that they can be used as guidelines for developing health portals.